Until recently, artificial intelligence (AI) and the Internet of Things (IoT) were buzzwords for selling updated versions of existing communications and technology products. But now, the use cases are coming thick and fast, especially as the two technologies combine into a unified technology called AIoT.
At Mobile World Congress in Barcelona this week, SAS Institute is demonstrating how AIoT can help everyday virtual assistants through artificial emotional intelligence and use image-based intelligence for public safety, among other.
During MWC 2019, SAS is showcasing the following use cases for advanced analytics and AI applications:
- Public Safety Monitoring powered by 5G and AI Computer Vision
The continued threat of terrorism all around the world has, in recent years, seen a change in tactics with more attacks performed utilising vehicle rampage, or mass gatherings of armed extremists targeting unsuspecting civilians going about their normal activities in our cities.
- Real time monitoring and AI/ML anomalies detection for Network Service Quality
With the upcoming virtual network functions (NFV) and virtualization of Telco networks, real time monitoring and anomalies detection plays an important role without which “closed loop monitoring” system are impossible to function. Finding the “pre failing signatures”, meaning a combination of network KPI’s which lead to service degradation, or complete stop, among thousands on network KPI’s is impossible without the use of machine learning algorithms and deep learning.
- Artificial Emotional Intelligence
After digital transformation programs, interactions between companies and their customers have been virtualized. They have lost knowledge
- Proactive Fraud Detection using AI
Telecommunications fraud is skyrocketing globally with over 30 billion dollars of losses reported last year by the CFCA. The biggest contributor to these fraud losses is Subscription and Dealer Fraud which typically account for 5-10% of annual revenues.
By harnessing the power of data analytics and machine learning, telecom operators can significantly reduce their fraud losses, optimize their operational costs and deliver a better, more secure customer experience.